PhD Colloquia Archive:

PhD Talk/Colloquia

Fall Semester

The PhD colloquium is a one-hour format, designed for practicing longer conference presentations or job talks. Participants in the spring colloquium will be candidates for the Best PhD Colloquium award.

**Seminars and Talks will be held in Towne 337 on Wednesdays at 12:00 PM unless otherwise specified.**

Abstract: We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are necessary in enabling time-sensitive control systems to operate over wireless links with high reliability. Achieving fast data rates over wireless links thus comes at the cost of reliability in the form of high packet error rates compared to wired links due to channel noise and interference. However, the effect of the communication link errors on the control system performance depends dynamically on the control system state. We propose a novel control-communication co-design approach to the low-latency resource allocation problem. We incorporate control and channel state information to make scheduling decisions over time on frequency, bandwidth and data rates across the next-generation Wi-Fi based wireless communication links that close the control loops. Control systems that are closer to instability or further from a desired range in a given control cycle are given higher packet delivery rate targets to meet. Rather than a simple priority ranking, we derive precise packet error rate targets for each system needed to satisfy stability targets and make scheduling decisions to meet such targets while minimizing total transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS) method is tested in numerous simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.

Bio: Mark Eisen received the B.Sc. degree in electrical engineering from the University of Pennsylvania, Philadelphia, USA in 2014. He is now working towards his PhD in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research interests include distributed optimization and machine learning. In the summer of 2013, he was a research intern at the Institute for Mathematics and its Applications at the University of Minnesota, Minneapolis, MN. Mr. Eisen was awarded Outstanding Student Presentation at the 2014 Joint Mathematics Meeting, as well as the recipient of the 2016 Penn Outstanding Undergraduate Research Mentor Award. He spent the summer of 2018 as an intern at Intel Labs in Hillsboro Oregon, working in the area of wireless industrial systems.

Anastasios TsiamisWednesday, October 10th"Secrecy Codes for Wireless Control Systems"

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Abstract: Because of its broadcast nature, the wireless medium is susceptible to eavesdropping. This raises confidentiality concerns in networked control systems, where many sensors and devices communicate wirelessly while carrying sensitive data about the system's operation. In this talk, we focus on eavesdropping attacks in a remote estimation scenario. An authorized user estimates the state of a linear system, based on the packets received from a sensor. Meanwhile, the packets may also be intercepted by an eavesdropper. Our goal is to design a coding scheme at the sensor which hides the state information from the eavesdropper. We present a new class of codes, termed "state-secrecy codes", which are specialized for dynamical systems. By applying properly designed linear transformations to the current and previously received states, they impose artificial unstable dynamics to the eavesdropper’s estimation scheme. As a result, under minimal conditions, they achieve secrecy in the estimation theoretic sense: the eavesdropper’s minimum mean square error converges to the maximum possible value, i.e. the open-loop prediction one when no message is received. Those conditions require that at least once, the user receives the corresponding packet while the eavesdropper fails to intercept it.

Bio: Anastasios Tsiamis received the Diploma degree in Electrical and Computer Engineering from the National Technical University of Athens, Greece, in 2014. Currently, he is a Ph.D. student in the Department of Electrical and Systems Engineering, University of Pennsylvania, working with prof. George Pappas. His research interests include control systems security, system identification, and learning for control.

Abstract: We consider a distributed optimization problem over a network of agents aiming to minimize a global objective function that is the sum of local convex and composite cost functions. To this end, we propose a distributed Chebyshev accelerated primal-dual algorithm to achieve faster ergodic convergence rates. In standard distributed primal-dual algorithms, the speed of convergence towards a global optimum (i.e., a saddle point in the corresponding Lagrangian function) is directly influenced by the eigenvalues of the Laplacian matrix representing the communication graph. In this paper, we use Chebyshev matrix polynomials to generate gossip matrices whose spectral properties result in faster convergence speeds, while allowing for a fully distributed implementation. As a result, the proposed algorithm requires fewer gradient updates at the cost of additional rounds of communications between agents. We illustrate the performance of the proposed algorithm in a distributed signal recovery problem. Our simulations show how the use of Chebyshev matrix polynomials can be used to improve the convergence speed of a primal-dual algorithm over communication networks, especially in networks with poor spectral properties, by trading local computation by communication rounds.

Bio: Jacob is a third year PhD student in the AMCS program at Penn advised by Victor Preciado and George Pappas. His current research interests include optimization and generalization in machine learning problems.

Abstract: Defaults have a powerful influence on human behavior. When companies ask employees to elect not to be enrolled in a retirement plan, rather than asking them to opt-in, enrollment nearly doubles. Default options are also seen to effect organ donorship, email marketing, HIV testing, and, unfortunately, research presentations.
PowerPoint defaults promote a topic-subtopic approach with word-filled slides and small figures. Cognitive scientists and psychologists have shown that these practices hinder audience understanding. And engineers and scientists know this; when asked to identify problems in peer presentations, they consistently identify these problems. Despite knowing the power of presentations for networking and promoting technical work, and easily identifying these problems in others’ slides, researchers consistently follow the defaults.
This presentation teaches the Assertion-Evidence approach to slide design. This approach is based on psychology and neuroscience research on learning and seeks to combat the consequences of default slide design. Experimental testing shows that this slide design approach improves material recall for the audience, drastically reduces audience misconceptions, and even improves presenter understanding.

Short Bio: Matthew received a B.S. in Mechanical Engineering from Penn State in 2016, with honors in Electrical Engineering. During his undergraduate career, he was trained in technical presenting as part of Engineering Ambassadors, an organization that conducts STEM outreach. After graduating, he worked for MIT Lincoln Laboratory doing research in the Homeland Protection. While at Lincoln Laboratory, he studied cognitive robotics with a professor in the Aeronautics and Astronautics Department at MIT. He is now a PhD student in the Department of Electrical and Systems Engineering at University of Pennsylvania with Vijay Kumar and George Pappas.

Abstract: This talk explores the privacy of cloud outsourced Model Predictive Control (MPC) for a linear system with input constraints. In our cloud-based architecture, a client sends her private states to the cloud who performs the MPC computation and returns the control inputs. In order to guarantee that the cloud can perform this computation without obtaining anything about the client's private data, we employ a partially homomorphic cryptosystem. We propose protocols for two cloud-MPC architectures motivated by the current developments in the Internet of Things: a client-server architecture and a two-server architecture. In the first case, a control input for the system is privately computed by the cloud server, with the assistance of the client. In the second case, the control input is privately computed by two independent, non-colluding servers, with no additional requirements from the client. We prove that the proposed protocols preserve the privacy of the client's data and of the resulting control input. Furthermore, we compute bounds on the errors introduced by encryption. We present numerical simulations for the two architectures and discuss the trade-off between communication, MPC performance and privacy.

Bio: Andreea Alexandru received the B.Eng. degree in Automatic Control and Systems Engineering from “Politehnica” University of Bucharest, Romania, in 2015. She is currently pursuing a Ph.D. degree in the Department of Electrical and Systems Engineering, University of Pennsylvania, working with prof. George Pappas and prof. Ali Jadbabaie. Her research interests lie in the security of control systems and private computations.

David HopperWednesday, November 7th "Optimizing Spin Readout of the Nitrogen-Vacancy Center in Diamond with Spin-to-Change Conversion"

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Abstract: The nitrogen-vacancy center in diamond is a mature platform for quantum technology, enabling sophisticated quantum information protocols as well as versatile quantum sensors operating in previously unreachable size and field regimes. The standard photoluminescence-based spin readout is fast (300 ns) but typical measurements yield only a few hundredths of a photon on average, necessitating tens of thousands of repeats to overcome shot noise in detecting the spin state. Spin-to-charge conversion (SCC) offers an alternative readout with significantly improved single-shot information. However, this benefit comes at the expense of orders of magnitude longer readout durations. Here, we present a framework for optimizing the SCC readout parameters that leads to dramatic reductions in overall measurement acquisition times. The improvements arise from the combination of an analytical charge readout model with numerical optimization of the overhead durations. In addition, our current work on incorporating real-time logic and classical signal processing for optimal sensor performance will be outlined. Finally, we discuss relevant applications such as spin relaxometry and control of nuclear registers and outline how other spin readout methods can benefit from this framework.

Bio: David received his B. Sc. In Physics from The Pennsylvania State University in 2014. He is now pursing his Ph. D. in Physics at the University of Pennsylvania, where he works in the Quantum Engineering Laboratory lead by Professor Lee Bassett. David’s research interests are in semiconductor quantum dynamics and designing systems that enable the utilization of quantum technology.

Abstract: Reproducing kernel Hilbert spaces (RKHSs) have been at the core of successful non-parametric tools in signal processing, statistics, and machine learning. Despite their success, the computational complexity of these models often hinders their use in practice. Fitting RKHS models
typically relies on the representer theorem to express the solution space as a combination of kernels evaluated at the training samples. Thus, the computational cost of evaluating these models is proportional to the number of training samples, which in many applications is
prohibitively high. Additionally, in functions with heterogeneous degrees of smoothness, the complexity is artificially kept high by the parts of the function that vary most. In this work we propose a method, which addresses both problems by obtaining a sparse representation which
adapts the smoothness and location of each kernel.

Bio: Maria Peifer is currently a PhD student in Prof. Alejandro Ribeiro's lab. She received her Master's degree in Electrical Engineering from Rutgers University and her bachelor degrees in Computer Engineering and Electrical Engineering from Drexel University. Her
research interests are in optimization, and multi-kernel learning.

Abstract: Low-capacity scenarios have become increasingly important in the technology of Internet of Things (IoT) and next generation of mobile networks. Such scenarios require efficient, reliable transmission of information over channels with extremely small capacity. Within these constraints, the performance of state-of-the-art coding techniques is far from optimal in terms of either rate or complexity. Moreover, the current non-asymptotic laws of optimal channel coding provide inaccurate predictions for coding in the low-capacity regime. In this paper, we provide the first comprehensive study of channel coding in the low-capacity regime. We will investigate the fundamental non-asymptotic limits for channel coding as well as challenges that must be overcome for efficient code design in low-capacity scenarios.

Bio: Mohammad is a second year PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He is also a M.Sc. student of statistics in Wharton school at the same time. Before joining Penn, he received two separate B.Sc. degrees in electrical engineering and pure mathematics as well as a M.Sc. degree in pure mathematics, all from Sharif University of Technology, Tehran, Iran. His research interests include coding and information theory, network and graph theory, optimization, and machine learning. His research interests in pure math include algebraic combinatorics, algebraic graph theory, Galois theory, advanced field and module theory, measure theory, topology, manifolds, and functional analysis.

Abstract: In cancer diagnosis and monitoring, “liquid biopsy” – searching for signatures of cancer in the blood – has been proposed as a non-invasive alternative to imaging and tissue sampling. Liquid biopsy allows cancer to be monitored more frequently and precisely. Many liquid biopsy strategies focus on circulating tumor cells (CTCs), which carry information about their parent tumor; however, they are extremely rare and must be distinguished from billions of other cells in the blood. Finding CTCs currently requires complex instrumentation and data analysis, often by highly trained experts. In this talk, we present an automated diagnostic that uses magnetic flow cytometry to find CTCs in whole blood, significantly reducing cost, size, and sample preparation requirements. An array of microscale graphene Hall sensors is integrated with parallelized CMOS circuitry and microfluidics to enable sensitive detection at high throughput. This talk will focus on the new fabrication, circuit, and system-level techniques developed in the course of designing the heterogeneous platform.

Bio: Vasant received a B.S. in electrical engineering from Caltech in 2017. He is now a PhD student in the Department of Electrical and Systems Engineering at the University of Pennsylvania, jointly supervised by David Issadore and Firooz Aflatouni. His research interests are in the design of new biomedical and clinical tools using approaches from microelectronics, photonics, and microfluidics.

Abstract: The nitrogen-vacancy (NV) center in diamond is the basis for emerging quantum technologies including sensing, quantum communication, and quantum computing. Many sensing modalities possible with the NV center, such as magnetometry and electrochemical potential sensing, have been demonstrated, paving the way to more targeted and dynamical sensing platforms. On the other hand, while the NV center has been heralded as one of the building blocks for quantum network systems, collection efficiency and scalability remain critical roadblocks. In this presentation, we examine ways to improve on these diamond-based systems through engineering. First, we explore the viability of site-specific, dynamic quantum sensing systems using nanodiamonds, leveraging their low toxicity and apparent availability for functionalization. We then investigate pathways to better collection efficiency and scalability in diamond systems through photonic structures. Finally, we provide an outlook on engineering NV- and diamond-based platforms in quantum sensing and communication.

Bio: Yung received their B.S.E in electrical engineering from Princeton University in 2015. They are now pursing their Ph. D. in Electrical and Systems Engineering at the University of Pennsylvania, where they work in the Quantum Engineering Laboratory lead by Professor Lee Bassett. Yung's current research interests are in designing and engineering solid-state systems for quantum sensing and communication.

Raj PatelWednesday, December 12th "Creation and Controls of Single Spins Hexagonal Boron Nitride"

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Abstract: Hexagonal boron nitride (hBN) is a van der Waals material which hosts visible fluorescent emitters at room temperature. These fluorescent emitters have been shown to be single-photon sources. Recently, some of these quantum emitters have exhibited spin-dependent fluorescence. The presence of single spins and single-photon sources at room temperature combined with low dimensionality makes hBN a unique material for applications such as quantum sensing and communication. However, it is necessary to identify and characterize the spin dynamics of these emitters. In fluorescent emitters, Optically Detected Magnetic Resonance (ODMR) can be used for understanding the spin dynamics. An understanding of how to create the emitters on-demand is also necessary for practical applications. These questions remain to be answered. In this talk, we present recent results and on-going experiments geared towards identifying and characterizing spin-based quantum emitters in hBN. We present results from recent observation of spin-dependent quantum emission in hBN, techniques to create and characterize these emitters, devices developed to perform ODMR and on-going experiments.

Bio: Raj received his M.Sc. in Physics and B.E. in Mechanical Engineering from BITS Pilani - Goa, India in 2015. He received an M.S.E. in Materials Science & Engineering from University of Pennsylvania in 2017. He is now pursuing his Ph.D. in Electrical & Systems Engineering at the University of Pennsylvania, where he works in the Quantum Engineering Laboratory lead by Prof. Lee Bassett. His research interests are in understanding spin-based quantum emitters in two-dimensional materials and other solid-state systems for quantum sensing and computation applications.

Abstract: Motion planning has had many algorithmic and theoretical developments and can reliably work for many robotic systems. However, high dimensional systems with new and interesting dynamics are still difficult to plan with. For these cases, we seek to combine machine learning techniques with traditional planning algorithms to either improve their speed or to plan with systems that are hard to model analytically. We will first present work in learning heuristics for random sampling based planners. These planners are more effective in high dimensional spaces but can depend on lot on heuristics to guide the search. An appealing approach is to learn these heuristics with data gathered from past experiences. We then present work on learning and using dynamics models. Learning a system model from data is highly useful for modeling systems that are hard to model analytically or require too many precise physical measurements. A new objective function for learning these models are presented that incorporate constraints on "sufficient accuracy."

Bio: Clark is a third year PhD student in ESE at Penn working with Alejandro Ribeiro and Daniel Lee. His research interests include incorporating machine learning techniques into traditional robotic algorithms to address their weaknesses. He received his B.S.E in Computer Engineering at the University of Michigan.